Apparatus and method for nondestructively inspecting fiberglass and nonmetallic pipes

ABSTRACT

A system and method for inspecting a composite material structure for defects includes a) an inspection apparatus having a heating device for heating a surface of the structure, an infrared camera for receiving radiation from the surface in response to heating, a controller configured to generate thermal images from the infrared radiation, b) a training system includes an arrangement for obtaining thermal images from a known composite material sample including a plurality of heating elements positioned to apply heat to an entire surface of the sample, an infrared camera for capturing thermal images of the sample, and a processing system for recording the thermal images in a training database, and c) a computer system coupled to the training system and the inspection apparatus adapted to receive thermal images from the inspection apparatus and detect parameters of defects in the structure using the training database.

FIELD OF THE INVENTION

The present invention relates to material inspection andcharacterization and in particular relates to an apparatus, system andmethod for inspecting composite structures.

BACKGROUND OF THE INVENTION

Composite materials (hereinafter “composites”) are currently used as areplacement for metallic materials in many industrial applicationsbecause of their resistance to corrosion. In the oil and gas industryfor instance, composites are used in filament wound composite structuressuch as pipes and vessel tanks. Examples of such composites includereinforced thermosetting resin (RTR) pipe (fiberglass),fiber-reinforced-polymer (FRP) systems, glass reinforced polymer (GRP)and glass reinforced epoxy (GRE). A cross-section and side view of partof a pipe made from a composite material is shown in FIGS. 14A and 14B.While use of composites is prevalent in low-pressure hydrocarbonapplications, there has been continued resistance in employing compositein high-pressure applications due to the difficulty in monitoringstructural integrity.

Given the susceptibilities of composites to certain types of damage,particularly in high-pressure applications, it is important toperiodically inspect composites to test whether such damage has occurredor is accumulating. It is also a requirement for the inspection to benon-destructive because it is infeasible to employ invasive techniquesthat interrupt the continued operation of the structures in the field.Suitable non-destructive testing (NDT) techniques should be able toaccurately detect typical defects in composites, be easy to apply, andpermit rapid and automated inspection of large areas. It would also beadvantageous for such techniques to provide in-service inspection withminimum surface preparation.

Among common NDT techniques, infrared thermography stands out as a goodcandidate since it provides contact-free measurement (no need forcoupling media), global and focused area scans, fast acquisition, andeasy operation. Limitations of the sensitivity of infrared thermographyequipment have until now restricted this technique to qualitative andboundary inspections, both of which are unable to provide accuratedefect size, depth data or data on the nature of any entrapped media,and are limited to detecting defects located close to the surfaces ofthe inspected structures.

There is therefore a need for non-destructive techniques for rapidly,reliably and cost-efficiently inspecting composite structures in anaccurate quantitative manner. The present invention is addressed to thisand related needs.

SUMMARY OF THE INVENTION

According to one embodiment, the present invention provides a system forinspecting a composite material structure for defects. The systemcomprises a) an inspection apparatus including a heating device forheating a section of a surface of the structure, an infrared camera forreceiving infrared radiation from the surface in response to heating, acontroller configured to generate thermal images from the receivedinfrared radiation, and a communication device; b) a training systemincluding an arrangement for obtaining thermal images from a knowncomposite material sample, the arrangement including a plurality ofheating elements positioned to apply heat to an entire surface of thesample, an infrared camera for capturing thermal images of the samplewhen heated, and a processing system for recording the captured thermalimages in a training database; and c) a computer system communicativelycoupled to the training system and the inspection apparatus, thecomputer system adapted to receive thermal images received from theinspection apparatus and to detect quantitative parameters of defects inthe structure using the training database.

In some embodiments, the heating elements of the training system arearranged on at least one circular rail and surround the sample in a360-degree manner.

In some embodiments, the computer system determines a possible presenceof a defect by comparing a distance between the infrared camera and apoint on the surface of the structure as calculated based on a) thethermal images versus b) images in the training database. In someimplementations, the computer system issues an alert to inspectionpersonnel if there is a threshold difference between the distancebetween the infrared camera and the point on the surface as calculatedby the thermal images versus the images in the training database.

In some embodiments, the infrared camera is movable around thecircumference of the sample, and is operative to acquire thermal imagesfrom the entire circumference of the sample. The arrangement forobtaining thermal images can also include a circular slider upon whichthe infrared camera is slidably coupled so as to move circumferentiallyaround the sample in a full 360 degrees.

The composite material structure is a pipe made of a composite material.In some implementations the composite material is one of reinforcedthermosetting resin (RTR), fiber-reinforced-polymer (FRP), glassreinforced polymer (GRP) and glass reinforced epoxy (GRE).

The present invention also provides a method for inspecting compositematerial structure for defects comprising. The method comprises traininga thermal image database by heating known composite material sampleswith a plurality of heating elements and capturing resulting thermalimages from the samples using an infrared camera; heating the structureto be inspected; capturing thermal images of the structure; deliveringthe thermal images to a computing system coupled to the thermal imagedatabase; matching the captured thermal images to images in the thermalimage database; and determining, at the computing system, whether thereare defects in the structure by comparing the thermal images of thestructure to matched thermal images in the thermal image database. Insome embodiments, an entire circumference of the structure is heated.

The step of determining whether there are defects can include comparinga distance between the infrared camera and a point on the surface of thestructure as calculated based on the captured thermal images versus thematched images on the training database. In some implementations themethod further includes alerting inspection personnel if there is athreshold difference between the distance between the infrared cameraand the point on the surface as calculated by the captured thermalimages versus the matched thermal images of the training database.

The infrared camera can be moved around the around the circumference ofthe sample to acquire thermal images from the entire surface.

These and other aspects, features, and advantages can be appreciatedfrom the following description of certain embodiments of the inventionand the accompanying drawing figures and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a system for inspecting compositestructures using quantitative infrared thermography according to anexemplary embodiment of the present invention.

FIG. 2A is a perspective view of an exemplary embodiment of aninspection apparatus according to the present invention.

FIG. 2B is a perspective view of another exemplary embodiment of aninspection apparatus according to the present invention that can movecircumferentially around an inspected structure.

FIG. 2C is a perspective view of another exemplary embodiment of aninspection apparatus according to the present invention that can moveboth circumferentially and longitudinally along an inspected structure.

FIG. 3 is a schematic diagram showing the components of an inspectionunit (chassis) of the inspection apparatus according to an exemplaryembodiment of the present invention.

FIG. 4A is a schematic diagram of an embodiment of a heating device andinfrared camera that can be employed in an inspection apparatusaccording to the present invention.

FIG. 4B is a graph showing exemplary activation input (above) andinfrared responses (bottom) according to a pulse thermographyactivation.

FIG. 4C is a graph showing exemplary activation input (above) andinfrared responses (bottom) according to a lock-in thermographyactivation.

FIG. 5 is a flow chart of a method of training an expert system tocorrelate virtual thermographs with characteristics of modeled defects(RVEs) according to an exemplary embodiment of the present invention.

FIG. 6 is schematic flow chart of a method of generating a defectivemicrostructure database (DVDB) according to an exemplary embodiment ofthe present invention.

FIG. 7A is a schematic perspective illustration of a representativevolume element (RVE) according to an exemplary embodiment of the presentinvention.

FIG. 7B is cross-sectional view of the RVE of FIG. 7A taken along axisA.

FIG. 7C is a cross-sectional of the RVE of FIG. 7A taken along axis B.

FIG. 8 is a flow chart of a method of automatically generating optimizedacquisition parameters according to an exemplary embodiment of thepresent invention.

FIG. 9 is a schematic flow chart of a method of generating a virtualthermograph database (VTDB) according to an exemplary embodiment of thepresent invention.

FIG. 10A is a schematic illustration of an exemplary matrix datastructure for storing thermograph data generated by according toembodiments of the present invention.

FIG. 10B is a schematic graphical illustration of an embodiment of thematrix data structure of FIG. 10A for a specific RVE.

FIG. 11 is a schematic diagram of an exemplary neural network that canbe employed in the expert system training method according toembodiments of the present invention.

FIG. 12 is a flow chart of a method for real time inspection of astructure according to an exemplary embodiment of the present invention.

FIG. 13 is a schematic diagram of a process of analyzing an acquiredthermograph using an expert system to yield defect parameters accordingto an exemplary embodiment of the present invention.

FIG. 14A is a photograph of avertical cross-section of a section ofpipeline made of composite material.

FIG. 14B is a front view of a section of pipeline made of compositematerial.

FIG. 15A is a perspective illustration showing an exemplary applicationof the inspection apparatus according to the present invention beingused to inspect an installed pipeline in the field.

FIG. 15B is a perspective illustration showing another exemplaryapplication of the inspection apparatus according to the presentinvention being used to inspect a pipeline as it is being produced in aproduction line.

FIG. 16A is a photograph of an end view of an arrangement for capturingthermal images of a composite material sample, for a training databaseaccording to an embodiment of the present invention.

FIG. 16B is a photograph showing an infrared camera being positioned tocapture images of a composite material sample.

FIG. 17 is a schematic view of a direct training system according to anembodiment of the present invention.

FIG. 18 is a flow chart of a method of inspecting a composite materialusing a training database according to one embodiment of the presentinvention.

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS OF THE INVENTION

A systematic approach to reliably and quantitatively inspectingstructures using infrared thermography is disclosed. The approachesdisclosed herein are particularly applicable for inspecting compositematerials. In some embodiments, the inspection system includes threedistinct elements: 1) a training system includes thermal images taken ofan entire circumference of known composite structures heated in acontrolled environment or that a) models structural defects of acomposite material, b) performs a mathematical simulation of how themodeled defects react to heating and which generates virtualthermographs (images indicative of temperature) showing temperaturechanges of the modeled defects over time, and c) correlates the virtualthermographs with parameters of the modeled defects using a machinelearning approach, producing an accessible virtual thermograph database;2) an inspection apparatus that is used at the site of the structure,and that includes a heating element to apply heat to a section of thestructure surface, and a recording device to record infrared radiationemitted from the heated section of the surface; and 3) an onsitecomputing system that receives thermal images of recorded infraredradiation from the inspection apparatus; and c) quantitativelydetermines the parameters of the received thermal images by comparingthe thermal images with images in the training system. Additionaldetails of the system are discussed in reference to the illustratedembodiments.

The disclosed system provides an integrated solution to the problem ofdetecting defects over composite structures with large and/or extendedsurfaces that is easy to implement, provides for fast inspection, and iseconomically efficient.

As a preliminary matter, the terms “thermograph” and “thermogram” areinterchangeable herein and both are to be interpreted as images of asurface area captured by an infrared camera or sensor in which a color,hue, gray scale or other differentiating mark indicates a specifictemperature or temperature range.

Inspection System

Turning to FIG. 1, an embodiment of a system 100 for inspectingcomposite structures using quantitate infrared thermography is shown.The system 100 includes an inspection apparatus 110 that is positionedproximate to a surface section 115 of a structure, which can be made ofa composite. The apparatus, described in greater detail below, heats thesurface section 115 and detects and records infrared radiation that isemitted from the section 115 in response to being heated. The inspectionapparatus 110 is communicatively coupled, preferably wirelessly, butoptionally by a wired connection, to a computer system 120. Computersystem 120 is operable to receive and process the data recorded by theinspection apparatus and is also communicatively coupled to a trainingsystem 130. The computer system 120 uses the data received from theinspection apparatus 110 and correlation information received from thetraining system 130 as inputs to a defect identification andquantification module (IDQ) 122, which generates a defect quantificationreport providing the type, size, depth, orientation and entrapped mediainformation for any defect identified on the structure surface. Thecomputer system 120 can be implemented onsite using any computing devicehaving sufficient processing and memory resources (e.g., a single ormulticore processor and solid-state memory), including a laptop, tabletor any other computing device readily accessible during an onsiteinspection.

In one embodiment, the training system 130 includes at least oneprocessor and an image capture apparatus. The apparatus obtains thermalimages from known samples under controlled thermal and environmentalconditions. The processor characterizes the thermal images based on theknown material properties of the sample and the thermal andenvironmental parameters of the training setup. A training databasecorrelating thermal images with material and thermal parameters isthereby collected and stored.

In another embodiment, the training system 130 includes at least oneprocessor that is operative to execute several modules. As will bedescribed in greater detail below, the modules include a defectmicrostructure database (DMDB) module 132 that comprises code thatcauses the at least one processor to use relevant inputs to generate aset of modeled structural defects, each defect of the database having aspecific type, size, depth, orientation and entrapped media. The defectsare stored in an associated DMDB database. The training system 130 alsoincludes a virtual thermograph database (VTDB) module 134 that comprisescode that causes the at least one processor to run mathematicalsimulations which calculate expected responses of the microstructuredefects within the DMDB database 132 to heating, and which causes the atleast one processor to generate virtual thermographs of the expectedinfrared radiation emissions from each of the microstructures. Thevirtual thermographs are stored in a VTDB database. The training system130 also includes an expert system module 136 that executes a machinelearning algorithm as may be implemented in the processor (e.g., ascomputer code), such as a neural network, to correlate the virtualthermographs output by the VTDB module 134 with the parameters of thedefects in the DMDB database 132. An optimized acquisition parameter(OAP) module 138 comprises code that causes the at least one processorto automatically determine optimal parameters for controlling theinspection apparatus 110 including optimal heating parameters such asheating mode, heating time, acquisition time, heat flux, etc. based oninputs including the properties of the inspected composite material andenvironmental and operating conditions. Modules 132, 134, 136, 138 caninclude and/or make use of processing resources for executing computerprogram instructions which generate data, and also employ memoryresources for storing the generated data. All of the processes executedby training system 130 can be executed before an inspection of an actualstructure.

The computing resources allocated for the training system 130 can beco-located on a single computing system or at a single facility or,alternatively, can be distributed across multiple computing systems andat a single or multiple facilities. Additionally, the training systemcan be hosted on fixed systems or can be hosted on the cloud on avirtual computing platform. In certain embodiments, distributedcomputing resources implement code that cause one or more of thecomputing resources to pause or cease one or more operations as afunction of the operational state or particular data of another one ofthe computing resources. In such an embodiment, computational resourcesare preserved by controlling operations in response to coordinatedcommunications among such resources in view of operational state updatesor particular data.

Inspection Apparatus

FIG. 2A is a perspective view of an embodiment of an inspectionapparatus 200 according to the principles disclosed herein. Theapparatus 200 is shown affixed to a pipe structure 205 made of acomposite that is to be inspected. Apparatus 200 includes adjustablesupporting clamps 210, 220 that are used to firmly and removablyposition and affix the apparatus 200 at a desired position on thestructure 205 to inspect a particular surface section. Clamps 210, 220are curved to adapt to structures having different circumferences. Theends of clamps 210, 220 terminate at respective suction pads e.g., 212,222 (pads on the reverse side of the structure 205 are not shown) orother suitable mechanism for firmly and removably affixing the clampends to the surface of the structure 205. A semi-enclosed chassis unit230 is coupled to and positioned between the clamps 210, 220. In theembodiment depicted the chassis unit 230 includes the components usedfor inspection as will be described further below. The chassis unit 230can be fixedly attached to the clamps 210, 220 by bar elements as shown,or alternatively, chassis unit 230 can be removably coupled to theclamps in other implementations.

FIG. 2B is a perspective view of another embodiment of an inspectionapparatus 250 according to the principles disclosed herein. In thisembodiment, clamps are replaced with slide guides 255, 260 that extendfurther around structure 205 and similarly end in respective suctionpads, e.g., 257, 262. A chassis unit 265 including the components usedfor inspection is coupled on first and second sides to sliding elements270, 275. In the embodiment depicted, the sliding elements 270, 275 areimplemented as semicircular-shaped components, each having a groove withwhich they mate to respective slide guides 255, 260. Sliding elements270, 275 include respective sets of wheels 272, 277 with which theymovably grip the surface of substrate 205. As depicted, the chassis unit265 is coupled to sliding elements which are movable circumferentiallyaround the structure as constrained by the slide guides 255, 260. Thisallows the chassis unit 265 to be carried circumferentially by movementof the sliding elements 270, 275. Wheels 272, 277 can be moved eithermanually or remotely (electronically) to actuate the sliding motion, andthe inspection apparatus 250 can be moved automatically around thecircumference to inspect a number of sections on the surface of thesubstrate sequentially. This enables the operator to scan large areas ofthe structure 205 while employing a single configuration and setup ofthe inspection apparatus 250.

FIG. 2C is a perspective view of a further embodiment of an inspectionapparatus 280 according to the present invention that provides bothcircumferential (rotational) and longitudinal movement (translation) ofthe apparatus 280 along a structure 205. Chassis 282 is coupled oneither side to sliding elements 284, 285. Spring elements 286, 287 areattached to sliding element 284 and springs elements 288, 289 areattached to the sliding element 285. Spring elements 286-289 can beimplemented using torsion springs. Springs 286-289 aid in fixing thechassis 282 at a specific position on structure 205 during aninspection. Latching arms 291, 292 are pivotably coupled to slidingelement 284, and latching arms 293, 294 are pivotably coupled to slidingelement 285. Wheels, e.g. 296, 297 are coupled to the bottom ofrespective sliding elements 284, 285 and wheels, e.g., 298, 299 arecoupled to the distal ends of latching arms 291, 292, 293, 294. Thewheels, e.g., 296-299 are preferably implemented using Omniwheels thatcan both slide and rotate on their axes enabling the apparatus 280 to bemoved either manually or remotely (electronically) in bothcircumferential and longitudinal directions with respect to structure205. This embodiment also enables the operator to scan large areas ofthe structure 205 while employing a single configuration and setup ofthe inspection apparatus 250.

FIG. 3 is a schematic diagram showing an embodiment of the components ofa chassis unit 300 that can be implemented in the apparatuses 200, 250,280 to perform the inspection of the composite structure by activeinfrared thermography. Active thermography involves heating the surfaceof an inspected area to create a difference between the temperature ofthe surface immediately above the defect and the surroundingtemperature. The heating produces an internal heat flux within a certaindepth of the surface. Subsurface defects affect the heat diffusion andproduce corresponding thermal contrasts which are reflected in theinfrared radiation emitted from the surface. Defects, which block andslow diffusion of heat within the material, are detected by the mannerin which the captured infrared radiation changes over time. Typically,sub-surface defects cause the surface immediately above the defect tocool at a different rate than the surrounding areas.

Turning to FIG. 3, the chassis unit 300 is semi-enclosed and the side ofthe unit that faces the structure surface is open at least in part topermit a heating device 310 and an infrared camera 320 to extendoutwardly from the enclosure of the housing toward the surface. Theheating device 310 is operable to emit radiation toward a section of thesurface during an inspection. An infrared camera 320 is operable todetect infrared radiation emitted back from the surface in response toheating. In some embodiments, infrared camera 320 has a spectral rangewithin 3.0 to 5.0 μm, minimal infrared pixels of 320×240 and sensitivityno greater than 20 mK. Both the heating device 310 and the infraredcamera 320 operate at a distance from the surface of the structure. FIG.4A is a schematic illustration of one implementation of the heatingdevice 310 and infrared camera 320. In this figure the heating device310 comprises two heating lamps 405, 410 arranged adjacent to oneanother so as to emit a cone of radiation 412 to cover an area ofsurface of a structure 415. The radiation causes a heat flux 420 beneaththe surface of structure 415, and infrared camera 320 is positionedcentrally to receive an optimal intensity of infrared radiation 425emitted from the surface. An exemplary defect 430 is shown located at adepth beneath surface 415. The heating device 310 can also include ahood 435 as a protection against the intense radiation emitted by theheating device 310. The infrared camera 320 is adjustable to optimizeacquisition of emitted infrared radiation and can be positionedcentrally between the heating lamps 405, 410 (as shown in FIG. 4A) oradjacent to the heating elements as shown in FIG. 3, and oriented atvarious angles with respect to the surface of the inspected structure.

Referring again to FIG. 3, chassis unit 300 also includes a controller330 (e.g., a microcontroller or processor) operative to control theheating device 310 and infrared camera 320. Controller 330 is alsocoupled to a memory unit 340 and to a transceiver 350 with which it iscommunicatively coupled to computer system 120 (of FIG. 1). Transceiver350 can conduct communication using various communication modesincluding Wi-Fi, RF and Zigbee protocols to achieve two-way datatransmission between the inspection apparatus and online computer system120.

FIG. 15A is an exemplary perspective illustration showing how theapparatuses described above with respect to FIGS. 2A-2C can be used inthe field. As shown, an exemplary apparatus 1500 according to thepresent invention is and positioned on the circumference of a pipe 1510for performing thermal inspection. The apparatus obtains eachlongitudinal section of the pipe as the apparatus moves, manually orautomatically, in the longitudinal direction along the pipe. FIG. 15B isan exemplary perspective illustration showing an alternative arrangementfor inspection during pipe production line. As shown a pipe 1520 movesalong a production line in the longitudinal direction shown by thearrow. Since the pipe moves longitudinally, the inspection apparatus1530 can be stationary with respect to the longitudinal direction whilehaving cameras that can rotate around the circumference of pipe 1520 toobtain comprehensive inspection coverage.

Heating lamps used for infrared thermography typically employ xenonflashtubes. During operation, lamps 405, 410 produce flashes of light inresponse to trigger signals from controller 330. After activating thelamps 405, 410, the controller 330 activates the infrared camera 320 toperiodically capture successive digital images of the radiativeemissions of the heated portion of the inspected surface. The infraredcamera 320 can be coupled to a motor operated by controller 330 tochange the angle and distance between the camera and the inspectedsurface to achieve a suitable focus on the surface. The digital imagedata generated by the infrared camera 320 can be transferred to andstored in memory unit 340. The controller 330 utilizes transceiver 350to transfer the digital image data from the memory unit 340 to computersystem 120. The controller 330 can also perform some pre-processing ofthe digital image data prior to transmission to computer system 120. Forexample, as the inspection apparatus is moved and images are capturedfrom adjacent surface sections, the controller 330 can format the datainto discrete image frames. Alternatively, such preliminary imageprocessing can be performed at computer system 120.

Among several active infrared known infrared thermography excitationmethods, pulsed thermography and lock-in thermography have been widelyused. FIG. 4B is a graph of the amplitude (intensity) of activationradiation provided over time (above), and amplitude of infraredradiation emitted from the surface over time (below). As indicated, inpulse thermography a pulse of high energy over short-duration is appliedto a surface and the amplitude of infrared radiation emitted back fromthe surface rises sharply in response, and then starts to fall as soonas the activation pulse ends. Presence of a defect is indicated by therelatively slower rate at which the amplitude of infrared radiationemitted from the surface declines (i.e., the slower rate at which thesurface cools). FIG. 4C is a similar graph of amplitude versus timeshowing a continuous, e.g., sinusoidal activation and a correspondingsinusoidal infrared response. As indicated, in lock-in thermography,presence of a defect is not shown in a different in amplitude response,but rather in a phase shift between the input activation energy and thesurface temperature response. The phase analysis of lock-in thermographyhas the advantage of being less sensitive to the local variations ofillumination or surface emissivity in comparison to pulsed thermography.However, either or both of pulsed and lock-in thermography as well asother excitation methods can be used.

In some implementations, laser thermal detection can be used to measuresurface temperature of the structure and/or to calibrate the heatsource. The recordings of a laser thermometer can be used toauto-calibrate the inspection device in real time.

As inspection of the composite structure is performed, with periodicheat activation and acquisition of infrared image data, the controller330 preferably receives and transfers the digital image data in realtime wirelessly as a video stream to computer system 120 for analysisand identification of defects.

Themography Training Method (Direct Method)

In one embodiment, a training database of thermal images is obtaineddirectly by obtaining thermal images from known samples that are heatedin a controlled setting, such as a laboratory. The images taken from thesamples recorded in association with the thermal and structuralproperties of the known samples, creating an internal training database.The data records in the training database can then be classified intosubsets based on temperature and material properties. FIG. 16A is aphotograph showing an end view of an apparatus 1600 used to capturethermal images from a sample to create a training database. A samplesection of a pipe 1605 is shown surrounded by heating elements e.g.,1610, 1615 positioned circumferentially on respective circular rails1620, 1625 so as to surround the sample. In this arrangement, theheating elements e.g., 1610. 1615 can cover the surface of the sample1605 over a complete 360-degrees and can heat the surface of the samplein a largely uniform isotropic manner. An infrared camera 1630 isslidably positioned on a circular slider rail 1635. The slider rail 1635has a base 1637 that can be positioned axially to directly face thesample 1605. Thereafter, the camera can be rotated to any radialposition with respect to the sample along the slider rail 1635. FIG. 16Bis a photograph showing the infrared camera 1630 positioned optimallywith respect to the sample 1605 and at approximately the 2 o'clockradial position.

FIG. 17 is a schematic view of the training system 1700 as a whole. Asample 1705 is heated by heating elements e.g., 1710, 1715 positionedcircumferentially along a circular rail 1717 to encompass the sample ina 360-degree manner. An infrared camera 1720 captures thermal imagesfrom the heated sample and delivers the images as digital data to acomputer system 1725 which analyzes and classifies the thermal images ina training database. A training database is generated that correlatesthe thermal images with material parameters of the known sample andthermal parameters of the known experimental training procedures.

FIG. 18 is a flow chart of a method for inspecting a composite structureusing a training database according to an embodiment of the presentinvention. After the method begins in step 1800, a section of thestructure to be inspected using an infrared camera is prepared in step1802. The section is heated in step 1804, and temperature and lightconditions are automatically detected (step 1806). In a next step 1808,the average distance between the infrared camera and the section isestimated, followed by the capturing of several images covering theentire circumference of the section by the infrared camera in step 1810.Thereafter, in step 1812, the captured images are matched and formattedto represent actual dimensions and locations. In step 1814, the capturedimages are matched with images in a training database and via thismatching and comparison, a distance between the camera and each surfacepoint on the section is estimated. In step 1816 it is determined whetherthe surface point distance is different from an average that would beexpected from the data of the training database. If it is different, theresult is filtered in step 1818 and an alarm is delivered to a fieldinspector to mark the surface area in question in step 1820. If thesurface point distance is not different from expectations, in step 1822the inspection moves another section of the structure. This process isrepeated until the entire structure is inspected (step 1824). In step1826, a report is generated that includes the data and findings of theinspection. In step 1828, the method ends.

Themography Training Method (Virtual Method)

In another embodiment, a virtual training method based on finite elementcan be used. FIG. 5 is a schematic flow chart of an embodiment of thetraining method 500 as disclosed herein. The training method includesseveral distinct procedures: i) input of relevant data by an operatorvia a user interface (510); ii) automatic configuration of internalparameters (520); iii) generation of a database of representativemicrostructures (DMDB) with integrated defects (530); iv) determinationof optimal setup parameters of the inspection apparatus for dataacquisition (540); v) generation of a virtual thermograph database(VTDB) by simulation (550); and vi) training of an expert system todetermine correlations between the microstructures of the DMDB and thethermographs of the VTDB generated by simulation (560). Each ofprocedures (i) to (vi) are described in turn. It is noted, however, thatin alternative embodiments, a subset of these procedures can beperformed without departing from the principles disclosed herein.

FIG. 6 is a schematic view of an embodiment of the first threeprocedures 510, 520, 530 of the training method outlined above. Asdepicted in step 510, inputs including material, structural andenvironmental properties are entered into the training system 130 by anoperator in order to model and store a set of representativemicrostructures containing specific defects. The possible materialproperties include parameters, such as, but not limited to: resin andfiber thermal conductivity, specific heat, fiber volume content,fraction of porosity, ply thickness, layup sequence, fiber orientationper ply, internal and/or external coating thickness. Input structuralproperties include the diameter and thickness of the material.Environmental and operating properties include parameters, such as, butnot limited to: operating pressure, transported fluid temperature andflow velocity, ambient temperature, and high temperature points inproximity to the inspected structure. In addition, the operator inputsset the defect type and entrapped media for each microstructure. Thepossible defect types include, among others: delamination, unique void,matrix cracking, fiber-matrix de-bonding, multiple voids, and holes.Entrapped media constitutes fluid or gas entrapped within the defects,which are typically air, water or oil. The parameters set forth areexemplary and do not constitute an exhaustive listing of all parametersor types that can be entered into the training system by operators.

In addition to the parameters entered by operators of the trainingsystem, the training system generates internal parameters in step 520.The internal parameters are used to initialize and configure a thermalsimulation model and can include, among other internal parameters, aselection from among: heat flux over the material surface over time,increments for defect size, depth location, minimum and maximum defectsize, minimum and maximum out-of-plane size, minimum and maximum depth,mesh discretization, and other thresholds for setting bounds on theparameters of defects. The internal parameters can be modifiable by theoperator.

The defect microstructure database (DMDB) module 132 uses the operatorinput and internally generates parameters, in step 530, to generate adatabase (DMDB) 605 that includes a number (N) of models of smallstructural elements, referred to herein as microstructures, e.g. 610,612, with each microstructure having specific parameters and at leastone integrated defect. The number (N) can also be controlled by theoperator through control over increment sizes. In some implementations,N is in a range of 1,000 to 50,000. However, a greater or smaller numberof microstructures can be generated. Each entry of the database, termeda “representative volume element” (RVE) can be parameterized as a vectorof eight elements V_(k) [a_(k), b_(k), c_(k), z_(k), θ_(k), φ_(k),D_(k), M_(k)] where z_(k) is the coordinate of the defect centroid inthe out-of-plane direction (perpendicular to the inspection plane) inthe kth RVE, a_(k), b_(k) and c_(k) are the spatial dimensions of thedefect within the kth RVE, θ_(k) and φ_(k) are the angles between theplane of the defect and the inspection plane, D_(k) is the defect type,and M_(k) is the type of media entrapped within the defect. FIG. 7A is aschematic representation of an example RVE defect stored in the defectmicrostructure database (DMDB). The defect 700 is modeled as anellipsoid in which z_(k) defines the location of the center of thedefect across the composite thickness, a_(k), b_(k), c_(k) define thelength, width and thickness of the defect and angles θ_(k) and ϕ_(k) incross-sectional planes A and B define the position and orientation ofthe defect with respect to the surface of the composite structure (theinspection plane). In the example depicted in FIG. 7A, the defect is anisolated delamination which is indicated by parameter D_(k), and theentrapped media is air, indicated by parameter M_(k). While the modelsimplifies the geometry of defects to some extent, the large number andvariation in location, sizes, defect types and entrapped media generatedin practice cover and suitably represent typical defects that occur incomposite structures. FIG. 7B is a cross-sectional view of RVE 700 takenalong axis A showing first orientation angle θ_(k) of the RVE withrespect to the inspection plane. FIG. 7C is an analogous cross-sectionview of RVE 700 taken along axis B showing a second orientation angleϕ_(k) of the RVE with respect to the inspection plane.

In step 540 of the training method 500, the optimized acquisitionparameter (OAP) module 138 uses the operator input including materialproperties and operating conditions as well as internally generatedparameters to determine optimal infrared thermography parameters forconfiguring an inspection apparatus. FIG. 8 is a flow chart of the OAPdetermination method 540. In a first step 810, the DMDB is searched andthe RVE that has the smallest and/or deepest defect is selected. In step820, the OAP module 138 determines initial and boundary conditions for athermal simulation model of the selected RVE using the initialparameters, which here are the parameters for heating flux (ΔH_(f)),heating period (ΔH_(p)), heating mode (e.g., continuous, modulated,pulsed) and camera acquisition time (Δt) generated in step 520 of thetraining method. However, it is noted that the heating parameters willdepend on the heating mode (e.g., flash, pulse, continuous). Forexample, in pulse mode, the frequency of the heating pulse will be acontrolled parameter.

In step 830, an analysis of thermal response of the least thermallyresponsive RVE of the DMDB (smallest and deepest defect) is performed.In some implementations, the thermal simulation employs finite elementanalysis. As will be understood by those of skill in the art, finiteelement analysis is a way to find approximate solution to boundary valueproblems for physical systems that involve partial differentialequations. Heat flow is characterized by partial differential equationsof this type and finite element analysis is often employed in providingsolutions in this field. Finite element analysis includes the use ofmesh generation techniques for dividing a complex problem into smallelements, as well as the use of a finite element simulation thatdetermines solutions to sets of equations for each of the finiteelements as well as a global solution to the entire domain. Followingcompletion of the thermal simulation of the selected least thermallyresponsive RVE, in step 840, the OAP module 138 determines, based on theinput parameters and thermal analysis, new optimized heating parameterssuch as, but not limited to ΔH_(f), ΔH_(p), Δt parameters, in theexample being discussed, in order to achieve a maximum temperaturecontrast during data acquisition.

The optimization of the heating parameters is iterative and the methodperforms a certain number of iterations before outputting optimizedvalues. Accordingly, in step 850 it is determined whether the number ofiterations performed thus far has reached a selectable threshold(MaxIterations). If MaxIterations has not been reached, the processflows back from step 840 to step 820. Alternatively, if MaxIterationshas been reached, in step 860 it is determined whether the value for thedetermined maximum temperature contrast (ΔT) remains lower than theinfrared camera sensitivity. If ΔT is lower than the camera sensitivity,in step 870, the OAP module 138 outputs: 1) the smallest diameterexpected to be detectable for a given depth; 2) the smallest expectedthickness detectable for a given depth; and 3) the greatest expecteddepth detectable within the breadth of a defect for a given defectdiameter. If ΔT is above the threshold, in step 880 the OAP moduleoutputs the current optimized values for heating parameters (e.g.,heating mode, ΔH_(f), ΔH_(p), Δt) from the last iteration of the method.

Returning to FIG. 5, the accumulated data entered or generated in steps510, 520, 530 and 540 are used as inputs in step 550, in which virtualthermograph module 134 executes a transient thermal analysis (TTA)simulation that outputs ‘virtual’ thermographs for each element (N) inthe DMDB. More specifically, as schematically illustrated in FIG. 9, theTTA simulator receives as inputs all of the elements in the defectivemicrostructure database 910 and the combined operator input,internally-generated parameters, boundary conditions and output of theOAP module (“combined inputs”). The TTA simulation is a parametric,mathematical model that can be implemented using finite elementanalysis. In such a finite element analysis, N separate analyses arecarried out corresponding to the N RVEs contained in the DMDB 910. Theoutput of each finite element analysis is a transient ‘virtual’thermograph of the outer surface of a structural element, i.e., a set ofgraphs showing thermal response of the surface over time. Generally, theexpected accuracy of the finite analysis depends on the number ofelements in the DMDB 910 (i.e., the value of N), with higher values of Nimproving the expected accuracy

The thermograph data is output and formatted as a matrix F_(ijk) in avisual thermograph database (VTDB) 940, where i represents the ithcamera pixel element, j represents the jth time increment, and krepresents the kth RVE. FIG. 10A provides an illustration of the datastructure of matrix F_(ijk). In the figure F_(ij1) represents all of theelements of matrix pertaining to the first RVE (k=1). Nested withinF_(ij1) are entries F_(i11) within which, in turn, are nested elementsF₁₁₁ through F_(n11). Elements F₁₁₁ through F_(n11) represent all of therecorded pixels during the first time increment for the first RVE (j=1,k=1). Accordingly, for each of the N RVEs there are associated m timeincrements, and during each time increment, n pixel values aregenerated. FIG. 10B is a schematic perspective illustration of athermograph at a given time increment, indicating how the thermographsfor a given RVE can be envisioned as a block of m thermographs, witheach thermograph having n pixels. As can be discerned, a high resolutionsimulation can generate a large amount of data. However, as the trainingsystem 130 performs analysis offline, there is no fixed limit to theresources that can be allocated to the transient thermal analysis.Moreover, the resolution level can be varied by the operator ifresources or efficiency are limiting factor in a particular scenario.

With a database of thermographs of sufficient precision and accuracy, itis possible to compare thermographs of a composite structure acquiredduring inspection runs in the field with thermographs in the database toidentify any defects present in the structure. However, it iscomputationally expensive to compare entire images for matching, andeven more so to compare the evolution of images (transient response)over time. One way to solve this problem is by training the system tocorrelate the virtual thermographs with the parameters of the RVEs fromwhich they are derived. In this way, when thermographs are acquired inthe field, they can be analyzed without having to search through animage database.

Therefore, in step 560 of the training method, an expert system istrained by a machine learning process to correlate the images of thevirtual thermograph database with the parameters of the RVEs from whichthey are derived. In some implementations, the expert system module 136of training system 130 employs a neural network algorithm, shown in FIG.11, as the machine learning technique. Neural network 1100 includes aninput layer 1110, one or more hidden layers 1120 and an output layer1130. The input layer 1110 includes all of the pixels of a virtualthermograph of the VTDB for a given RVE at a particular time increment,and the output layer 1130 includes the parameters of the same RVEincluding its position, orientation, defect dimensions, defect type andentrapped media. The neural network correlates the input layer 1110 tothe output layer 1130 by use of one or more hidden layers 1120. Each ofthe inputs in the input layer 1110 is multiplied by coefficient factorsin the hidden layer(s) 1120 to yield the output layer 1130. Thecoefficients of the hidden layers 1120 are determined by a process ofbackward propagation in which a cost function is minimized. This yieldsan optimized correlation between the virtual thermographs and the RVEparameters. The expert system module 136 stores the coefficients forfurther use. After the expert system training is complete, the trainingmethod ends in step 570.

Real-Time Inspection Method

Flow charts of the sub-parts of a real time inspection method 1200performed by the online computer system 120 and inspection apparatus110, respectively, are shown in FIG. 12. As noted above, the expertsystem is generated and stored off-site at a remotely located facility.In order for operators at a field site to perform a structuralinspection to be able to utilize the expert system, access to the expertsystem at the onsite location is required. In a first step 1205, anoperator obtains access to the expert system either by logging into anexpert system server over a network using online computer system oralternatively, by directly downloading the expert system algorithm andstored data from the training system 130 onto the online computer system120. Additionally, the expert system can be downloaded by a using astorage medium such as a flash drive. In step 1210, the online computersystem uploads optimized acquisition parameters from the OAP module 138of training system 130. In a following step 1215, the online computersystem 120 transmits the optimized acquisition parameters to thetransceiver 350 of inspection apparatus 110.

In step 1255, inspection apparatus 110 receives the optimizedacquisition parameters from online computer system 120. Using theacquired parameters, in step 1260, the controller 330 of inspectionapparatus 110 configures heating and acquisition parameters foroperating the heating device 310 and infrared camera 320. Uponconfiguration, the inspection apparatus is configured to apply radiationand capture infrared radiation for the smallest and deepest defect thatis within the detection capability of the infrared camera, so that theinspection apparatus as a whole has maximum sensitivity for the givenhardware capabilities. In step 1265, the inspection apparatus performsan inspection in which a section of an inspected surface is heated byheating device 310 and infrared radiation acquired by infrared camera320. During inspection, the inspection apparatus can be fixed inposition to inspect a specific area of a structure, or the inspectionapparatus can be controlled to move in a particular trajectory toinspect different areas or the entire surface of a structure. In realtime or approximate real time, in step 1270, the controller compiles theinfrared radiation data acquired by the infrared camera and transmitsthe data in the form of thermographs to computer system 120 viatransceiver 350.

More specifically, for any given longitudinal position, several highresolution thermal images can be captured in various radial positions tocover the entire circumference of a structural section; the individualradial images are matched together to form a single image representingthe unfolded structure. Damage markers can be added to the images forfurther processing and screening.

Computer system 120 receives the thermographs in step 1220, and in step1225, performs real-time quantification of defects in the inspectedstructure based on the acquired thermographs. Step 1225 is schematicallyillustrated in FIG. 13 which illustrates a thermograph 1310 input toexpert system 1320. Expert system 1320 in this case is the moduleexecuted on computer system 120 (as opposed to the training system 130)and, as noted above, can represent a client of an expert system server,or a software module executed on computer system 120 that emulatesaspects of the expert system module 136 of the training system 130. Insome implementations, expert system 1320 can be a copy of the expertsystem module 136 uploaded from the training system 130. Expert system1320 applies the correlations obtained from the training system 130 tothe acquired thermograph and outputs a defect parameter vector includingthe elements described above with reference to FIG. 7. The defectparameter vector identifies the defect in terms of its type, size,depth, orientation and entrapped media. The online computer system then,in step 1230, generates a defect quantification report that includes thethermographs acquired in real time and the characteristics of anydetected defects. The defects can be characterized in terms ofclassification, in terms of depth in a single dimension, in terms ofarea in two dimensions or volume in three dimensions.

The quantification report can also include a 3-dimensional rendering ofthe locations of defects, including axial position, reference point ofstart of inspection, radial position and the depth of the damagelocation within the structure.

The disclosed apparatus, system and methods for inspecting structuresusing quantitative infrared thermography provide several advantageousfeatures. The system and methods are easy to implement as, in someembodiments, the inspection apparatus can move automatically around andalong the inspected structure, reducing manual inspection procedures. Inaddition, embodiments of the inspection apparatus are designed toprogress rapidly over inspected structures, further reducinginterventions in the inspection process. The disclosed system alsodelivers inspection results in real-time, allowing the possibility ofinitiating remedial measures onsite to remove serious defects. Theinspection apparatus is contact free and relatively cost effective; theinfrared camera is the highest expense in most implementations.Moreover, the system provides unbiased configuration of the inspectionapparatus since optimization parameters for data acquisition aredetermined by the system independently from the operator. Likewise,inspection results are unbiased as they are generated independently fromhuman expert knowledge or expertise.

While the apparatus, system and methods disclosed herein areparticularly intended to be used for composite inspection and defectdetection, with suitable modifications, the inventive techniques can beapplied to other materials.

It is to be understood that any structural and functional detailsdisclosed herein are not to be interpreted as limiting the apparatus,system and methods, but rather are provided as a representativeembodiment and/or arrangement for teaching one skilled in the art one ormore ways to implement the methods.

It is to be further understood that like numerals in the drawingsrepresent like elements through the several figures, and that not allcomponents and/or steps described and illustrated with reference to thefigures are required for all embodiments or arrangements

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising”, when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

Terms of orientation are used herein merely for purposes of conventionand referencing, and are not to be construed as limiting. However, it isrecognized these terms could be used with reference to a viewer.Accordingly, no limitations are implied or to be inferred.

Also, the phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” or “having,” “containing,” “involving,” andvariations thereof herein, is meant to encompass the items listedthereafter and equivalents thereof as well as additional items.

While the invention has been described with reference to exemplaryembodiments, it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted forelements thereof without departing from the scope of the invention. Inaddition, many modifications will be appreciated by those skilled in theart to adapt a particular instrument, situation or material to theteachings of the invention without departing from the essential scopethereof. Therefore, it is intended that the invention not be limited tothe particular embodiment disclosed as the best mode contemplated forcarrying out this invention, but that the invention will include allembodiments falling within the scope of the appended claims.

What is claimed is:
 1. A system for inspecting a composite materialstructure for defects comprising: an inspection apparatus including aheating device for heating a section of a surface of the structure, aninfrared camera for receiving infrared radiation from the surface inresponse to heating, a controller configured to generate thermal imagesfrom the received infrared radiation, and a communication device; atraining system including an arrangement for obtaining thermal imagesfrom a known composite material sample, the arrangement including aplurality of heating elements positioned to apply heat to an entiresurface of the sample, an infrared camera for capturing thermal imagesof the sample when heated, and a processor configured to establish aplurality of correlations between known parameters of the compositematerial samples and the obtained thermal images; and a computer systemcommunicatively coupled to the training system and the inspectionapparatus, the computer system adapted to receive thermal imagesreceived from the inspection apparatus and to detect quantitativeparameters of defects in the structure using the training database. 2.The system of claim 1, wherein the heating elements of the trainingsystem are arranged on at least one circular rail and surround thesample in a 360-degree manner.
 3. The system of claim 1, wherein thecomputer system determines a possible presence of a defect by comparinga distance between the infrared camera and a point on the surface of thestructure as calculated based on a) the thermal images versus b) imagesin the training database.
 4. The system of claim 3, wherein the computersystem issues an alert to inspection personnel if there is a thresholddifference between the distance between the infrared camera and thepoint on the surface as calculated by the thermal images versus theimages in the training database.
 5. The system of claim 1, wherein theinfrared camera is movable around the circumference of the sample, andis operative to acquire thermal images from the entire circumference ofthe sample.
 6. The system of claim 5, wherein the arrangement forobtaining thermal images includes a circular slider upon which theinfrared camera is slidably coupled so as to move circumferentiallyaround the sample in a full 360 degrees.
 7. The system of claim 1,wherein the composite material structure is a pipe made of a compositematerial.
 8. The system of claim 7, wherein the composite material isone of reinforced thermosetting resin (RTR), fiber-reinforced-polymer(FRP), glass reinforced polymer (GRP) and glass reinforced epoxy (GRE).9. A method for inspecting composite material structure for defectscomprising, the method comprising: training a thermal image database byheating known composite material samples with a plurality of heatingelements and capturing resulting thermal images from the samples usingan infrared camera to establish correlations between the compositematerial samples with known parameters and thermal images of thesamples; heating the structure to be inspected; capturing thermal imagesof the structure; delivering the thermal images to a computing systemcoupled to the thermal image database; matching the captured thermalimages to images in the thermal image database; and determining, at thecomputing system, whether there are defects in the structure bycomparing the thermal images of the structure to matched thermal imagesin the thermal image database.
 10. The method of claim 9, wherein anentire circumference of the structure is heated.
 11. The method of claim9, wherein the step of determining whether there are defects includescomparing a distance between the infrared camera and a point on thesurface of the structure as calculated based on the captured thermalimages versus the matched images of the training database.
 12. Themethod of claim 11, further comprising alerting inspection personnel ifthere is a threshold difference between the distance between theinfrared camera and the point on the surface as calculated by thecaptured thermal images versus the matched thermal images of thetraining database.
 13. The method of claim 9, further comprising movingthe infrared camera around the circumference of the sample to acquirethermal images from an entire surface of the sample.
 14. The method ofclaim 9, wherein the composite material structure is a pipe made of acomposite material.
 15. The system of claim 14, wherein the compositematerial is one of reinforced thermosetting resin (RTR),fiber-reinforced-polymer (FRP), glass reinforced polymer (GRP) and glassreinforced epoxy (GRE).
 16. The system of claim 1, wherein thecorrelations pertain to material properties of the samples and thermaland environmental parameters describing conditions under which thethermal images of the samples are obtained by the training system.